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Oral Session I - IV : Security in Vehicular and Cloud Environments

Machine Learning Driven Traffic Congestion Control Approach in VANETs

첫 페이지 보기
  • 발행기관
    한국차세대컴퓨팅학회 바로가기
  • 간행물
    한국차세대컴퓨팅학회 학술대회 바로가기
  • 통권
    The 10th International Conference on Next Generation Computing 2024 (2024.11)바로가기
  • 페이지
    pp.207-210
  • 저자
    Muhammad Ubaid Ullah, Attia Amin, Muhammad Sajid Farooq, Muhammad Saleem, Javaid Ahmad Maik, Muhammad Adnan Khan
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A468845

원문정보

초록

영어
Addressing traffic congestion in Vehicular Adhoc Networks (VANETs) is crucial for ensuring safety, social welfare, and economic progress. This study introduces a novel approach utilizing transfer learning in conjunction with the Gradient Boosting algorithm to optimize information transmission within VANETs. By leveraging pre-trained nodes as information sources, the proposed model effectively trains newly registered nodes, enhancing congestion control performance. Simulation results conducted in Python demonstrate the model's effectiveness, showcasing reduced execution times compared to traditional fuzzy logic-based methods. Integration of this model into existing congestion control systems promises real-time congestion screening capabilities. The study highlights the importance of further research collaboration to tackle realtime implementation challenges and advance traffic congestion management using AI-based techniques. Simulation results have indicated that the proposed system model achieves a performance of 95.43% accuracy. It also noted that the use of the proposed system in producing the HRA results is more accurate compared to the past methods.

목차

Abstract
I. INTRODUCTION
II. LITERATURE REVIEW
III. PROPOSED METHODOLOGY
IV. SIMULATION RESULTS
V. CONCLUSION
VI. FUTURE WORK AND LIMITATIONS
VII. REFERENCES

키워드

VANET RSU V2V ANN CNN

저자

  • Muhammad Ubaid Ullah [ University of South Asia, Lahore, Pakistan ]
  • Attia Amin [ Department of Computer Science NCBA&E Lahore, Pakistan ]
  • Muhammad Sajid Farooq [ Department of Cyber Security, NASTP Institute of Information Technology Lahore (NIIT), Lahore, 54000, Pakistan ]
  • Muhammad Saleem [ School of Computer Science, Minhaj University Lahore Lahore, Pakistan ]
  • Javaid Ahmad Maik [ Department of Computer Science, NCBA&E Lahore, Pakistan ]
  • Muhammad Adnan Khan [ School of Computing, Skyline University College, Sharjah, United Arab Emirates. RSCI, Riphah International University, Lahore 54000, Pakistan ]

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    한국차세대컴퓨팅학회 [Korean Institute of Next Generation Computing]
  • 설립연도
    2005
  • 분야
    공학>컴퓨터학
  • 소개
    본 학회는 차세대 PC 및 그 관련분야의 학술활동을 통하여 차세대 PC의 학문 및 기술발전을 도모하고 산업발전 및 국제협력 증진을 목적으로 한다.

간행물

  • 간행물명
    한국차세대컴퓨팅학회 학술대회
  • 간기
    반년간
  • 수록기간
    2021~2025
  • 십진분류
    KDC 566 DDC 004

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